In many applications, data can be provided in a time series (data streams), in which data values are provided in a series of time points. Example applications in which data can be expressed in time series include financial applications (e.g., time series of asset prices, revenue, profit, currency exchange rates, etc.), network monitoring (e.g., metrics regarding performance of various aspects of a network, performance metrics of servers, performance metrics of routers, etc.), and so forth.
Customer and database administrators (or other users) often have to digest and visualize long multi-dimensional time series data, such as data reflecting workload management, network performance, computer performance, database loading error rates, and so forth. The time series data is analyzed to discover patterns, trends, and anomalies.
Although various types of charts can be used to visualize time series data, conventional visualization techniques often are unable to adequately display a sufficiently large number of time intervals for long, multi-dimensional time series, particularly when the time series is continually growing. As a result, users are unable to effectively analyze or visualize data patterns, trends, and anomalies in a single view.